Validity of a 3-dimensional body scanner: comparison against a 4-compartment model and dual energy X-ray absorptiometry
Bibliographic record
Abstract
Three-dimensional (3D) body scanner technology for body composition assessment is expanding. The aim of this study was to assess the validity of a 3D body scanner. One hundred and ninety-four participants (43% male; age: 23.52 ± 5.47 years; body mass index: 23.98 ± 3.24 kg·m −2 ) were measured using 3D scanner and a 4-compartment (4C) model utilizing dual-energy X-ray absorptiometry (DXA), air displacement plethysmography, and bioelectrical impedance spectroscopy. Dependent t tests, validity statistics including total error (TE), standard error of the estimate, constant error, and Bland–Altman analyses were utilized. Compared with 4C, 3D scanner fat mass (FM) [mean difference (MD; 3D−4C): 2.66 kg ± 3.32 kg] and percent body fat (%BF) (MD: 4.13% ± 5.36%) were significantly (p < 0.001) over-predicted; fat free mass (FFM) was significantly underpredicted (MD: −3.15 kg ± 4.75 kg; p < 0.001). 3D demonstrated poor validity indicated by TE (%BF: 5.61%; FM: 4.50 kg; FFM: 5.69 kg). In contrast, there were no significant differences between 3D and DXA measures; 3D scanner demonstrated acceptable measurement for %BF (TE: 4.25%), FM (TE: 2.92 kg), and lean mass (TE: 3.86 kg). Compared with the 4C criterion, high TE values indicated 3D estimates were not valid. In contrast, 3D estimates produced acceptable measurement agreement when compared with DXA; an average overestimation of %BF by 5.31% (vs. 4C) and 4.20% (vs. DXA) may be expected. Novelty: 3D body composition estimates are not valid compared with the 4-C criterion model. 3D estimates appeared to be more valid in females, compared with males. When compared with DXA, 3D estimates were acceptable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".